Objective To investigate the correlation between muscle mass and gait parameters in patients with cerebral small vessel disease(CSVD),as well as the impact of reduced muscle mass on the occurrence of falls in CSVD patients.Methods This study was employed a cross-sectional design.Ninety-five inpatients with CSVD confirmed by the Department of Neurology of the Seventh Medical Center of Chinese People's Liberation Army General Hospital from January 1,2022 to June 1,2023 were included consecutively.The 95 patients with CSVD were divided into two groups,namely the reduced muscle mass group and the normal muscle mass group,based on the criteria of appendicular skeletal muscle mass(ASM)≤7.0 kg/m2 for males and ASM ≤5.7 kg/m2 for females as reduced muscle mass.Baseline data(sex,age,years of schooling,number of accompanying diseases[hypertension,hyperlipidemia,diabetes,angina pectoris,myocardial infarction,and migraines]),cognitive function assessment results(mini-mental status examination[MMSE],verbal fluency test[VFT],clock drawing test[CDT],and trail-making test part-B[TMT-B]),gait characteristics(basic gait parameters[gait speed,stride time,stride length,stride frequency]and reanalysis gait parameters[variation coefficient of gait speed,stride time,stride length,stride frequency,and time-phase coordination index,gait asymmetry index]),CSVD imaging findings(cerebral microbleeds,lacunar infarcts,and white matter hyperintensities),and history of falls.The differences in baseline data,cognitive function assessment results,and gait characteristics between the reduced muscle mass group and the normal muscle mass group were compared and analyzed.Linear regression was used to analyze the correlation between muscle mass and gait parameters.The 95 CSVD patients were divided into fall group and non-fall group,and the differences in baseline data,cognitive function assessment results,gait characteristics,CSVD imaging findings,and muscle mass between the two groups were compared.Binary Logistic regression analysis was used to evaluate the impact of reduced muscle mass on falls.Results(1)The majority of patients in the reduced muscle mass group were females(67.7％[21/31]).There was a statistically significant difference in the sex distribution between the reduced muscle mass group and the normal muscle mass group(x2=6.143,P=0.013).There were no statistically significant differences in the other baseline characteristics and cognitive function between the two groups(all P＞0.05).(2)Compared to the normal muscle mass group,patients in the reduced muscle mass group had slower gait speed([0.72±0.16]m/s vs.[0.94±0.15]m/s),longer stride time([1.22±0.12]s vs.[1.08±0.08]s),shorter stride length([0.84±0.19]m vs.[1.00±0.14]m),and lower step frequency([100±9]steps/min vs.[112±8]steps/min).The coefficients of variation for gait speed(11.579[8.163,15.870]％vs.7.304[5.873,9.959]％),stride time(3.876[2.778,5.769]％vs.2.480[1.874,3.001]％),stride length(7.800[5.400,10.700]％vs.5.600[4.100,7.950]％),step frequency(5.313[3.568,7.272]％vs.3.674[3.099,5.082]％),and time-phase coordination index(5.894[4.392,9.080]％vs.3.828[3.031,5.972]％)were all increased,and the differences were statistically significant(all P＜0.05).There was no statistically significant difference in gait asymmetry index between the two groups(P＞0.05).Further analysis with sex and lacunar infarction as potential confounding factors showed that there were statistically significant differences in baseline gait parameters between the normal muscle mass group and the reduced muscle mass group(all P＜0.01).In the reanalysis of gait parameters,only the differences in the coefficients of variation for gait speed and stride time were statistically significant(both P＜0.05).(3)When analyzing ASM as a continuous variable,age and CDT as potential confounders,and stratifying by sex,the results showed that in male patients,baseline gait parameters(gait speed,stride time,stride length,and step frequency with 95％CI ranging from 0.057 to 0.152,-0.105 to-0.023,0.013 to 0.097,and 1.686 to 8.854,respectively),as well as coefficients of variation for stride time(95％CI-0.016 to-0.003)and stride length(95％CI-0.026 to-0.006),were correlated with muscle mass reduction(all P＜0.05).In female patients,gait speed(95％CI0.034 to 0.166)and coefficient of variation for gait speed(95％CI-0.059 to-0.010),stride time(95％CI-0.110 to-0.011),coefficient of variation for stride time(95％CI-0.025 to-0.001),and stride length(95％CI 0.018 to 0.163)were correlated with muscle mass reduction(all P＜0.05).(4)Muscle mass reduction was an independent risk factor for falls(OR,5.044,95％CI 1.840 to 13.827,P=0.002).Conclusions The preliminary analysis of this study suggests that there is a certain correlation between muscle mass and gait parameters in patients with CSVD.Additionally,the study indicates that a decrease in muscle mass among CSVD patients may increase the risk of falls.Therefore,it is important to prioritize the management of muscle mass in CSVD patients.

Objective:To compare the gait characteristics of cognitive and motor dual task walking (DTW) in patients with cerebral small vessel disease (CSVD), and determine the best gait parameters to diagnose CSVD and judge the severity of the disease.Methods:A total of 106 patients with CSVD and 21 healthy individuals were included from September 1, 2020 to July 1, 2021 in the Seventh Medical Center of Chinese People′s Liberation Army General Hospital. According to the Fazekas scores, the subjects were divided into mild ( n=34, 1 point), moderate ( n=34, 2 points), severe ( n=38,3 points) groups and control group ( n=21). Participants were recorded parameters under single task walking (STW) and DTW conditions, and calculated dual task effect (DTC) through the difference between single task and dual task. The differences in gait variances and their DTC were shown by generalized estimation equations when performed in STW and DTW and 4 groups of the severity of disease. Post-hoc comparisons were corrected using Bonferroni′s method. Spearman analyses were applied to explore the correlations between gait parameters and their DTC during STW or DTW and severity of disease. Based on the Logistic model, combining predictors or probabilities were gained and applied to establish receiver operating characteristic curve in order to calculate sensitivity, specificity, and the area under the curve. Results:In the control group, there was no statistically significant difference in gait parameters between STW and DTW. In the CSVD group, the gait parameters of STW were significantly better than cognitive or motor DTW (all P<0.05). In the control group, there was no statistically significant difference in basic gait parameters under different tasks (all P>0.05). In cognitive DTW, temporal gait parameters (stride frequency and stride time) deteriorated significantly only in moderate and severe groups [stride frequency:moderate group 100.220±1.795/min,severe group 94.525±2.139/min;stride time:moderate group (1.227±0.024) s, severe group (1.299±0.031) s], but spatial parameters [stride length: control group (1.050±0.021) m, mild group (0.974±0.022) m, moderate group (0.903±0.025) m, severe group (0.793±0.026) m; stride speed: control group (0.944±0.028) m/s, mild group (0.866±0.030) m/s, moderate group (0.751±0.027) m/s, severe group (0.606±0.022) m/s] were significantly different among all groups (except the control group and mild group;all P<0.05). The DTC of all gait parameters during cognitive DTW was higher than that during motor DTW (all P<0.05) for CSVD patients. While no any difference was found between cognitive DTW and motor DTW in the control group (all P>0.05). Similarly, the temporal parameters′ DTC of cognitive DTW was abnormal only in the late stage of disease, while the spatial parameters′ DTC showed statistically significant difference among all the groups (including the control group and the mild group;all P<0.05). Correlation coefficients of the spatial parameters and their DTC in condition of cognitive DTW were significantly higher than temporal parameters and their DTC (0.50< r<0.64 vs 0.15< r<0.39). The area under curve of the combined predictor was significantly higher than that of any single index. Conclusions:Cognitive DTW can better reflect the abnormal gait of CSVD patients. The spatial parameters and DTC of cognitive DTW could effectively diagnose CSVD and distinguish the disease of severity. And DTC might be better indicators. For diagnosis of CSVD, there was no significant discrepancy between the spatial parameters and DTC, but the combined predictor could significantly improve the sensitivity and reduce the false negative rate.